A large body of mathematical and computational techniques has been developed in the area of reliable signal transmission through noise-introducing channels. These different techniques depend on assumptions made with regard to the noise-introducing channel, as well as on the amount and nature of information available, during denoising, regarding the original signal. The denoising process may be characterized by various computational efficiencies, including the time complexity and working-data-set complexity for a particular computational method, as well as by the amount of distortion, or noise, remaining in a recovered signal following denoising with respect to the originally transmitted, clean signal.
Although methods and systems for denoising noisy signals have been extensively studied, and signal denoising is a relatively mature field, developers, vendors, and users of denoising methods and systems, and of products that rely on denoising, continue to recognize the need for improved denoising techniques. In particular, there is a need for methods and systems for recovering a signal from its noise-corrupted observations where the noise-corrupted observations are of a general or continuous alphabet.